https://github.com/OpenLMLab/GAOKAO-Bench
GAOKAO-Bench is an evaluation framework that utilizes GAOKAO questions as a dataset to evaluate large language models.
https://github.com/OpenLMLab/GAOKAO-Bench
Last synced: 27 days ago
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GAOKAO-Bench is an evaluation framework that utilizes GAOKAO questions as a dataset to evaluate large language models.
- Host: GitHub
- URL: https://github.com/OpenLMLab/GAOKAO-Bench
- Owner: OpenLMLab
- License: apache-2.0
- Created: 2023-04-16T03:36:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-07T02:59:39.000Z (3 months ago)
- Last Synced: 2025-03-17T15:08:12.442Z (29 days ago)
- Language: Python
- Homepage:
- Size: 12.8 MB
- Stars: 617
- Watchers: 6
- Forks: 44
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-instruction-datasets - GAOKAO - in-the-blank_Questions](https://github.com/OpenLMLab/GAOKAO-Bench/tree/main/data/Fill-in-the-blank_Questions) \| [Multiple-choice_Questions](https://github.com/OpenLMLab/GAOKAO-Bench/tree/main/data/Multiple-choice_Questions) \| [Open-ended_Questions](https://github.com/OpenLMLab/GAOKAO-Bench/tree/main/data/Open-ended_Questions) | OpenLMLab | 2785 | CN | MT | COL | Multiple-choice, Fill-in-the-blank and Open-ended questions from examination | human annotated | [download](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/GAOKAO) | (Statistics)
- awesome-llm-eval - GAOKAO-Bench - bench是一个以中国高考题目为数据集，测评大模型语言理解能力、逻辑推理能力的测评框架 | (Datasets-or-Benchmark / 通用)
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- awesome-production-machine-learning - GAOKAO-Bench - Bench.svg?style=social) - GAOKAO-Bench is an evaluation framework that uses Chinese National College Entrance Examination (GAOKAO) questions as a dataset to assess large models' language comprehension and logical reasoning abilities. (Evaluation and Monitoring)
README
# GAOKAO-Bench
GAOKAO-Bench是一个以中国高考题目为数据集，测评大模型语言理解能力、逻辑推理能力的测评框架。[[Read In English]](./README_EN.md)[[paper]](https://arxiv.org/abs/2305.12474)
## 更新
[[GAOKAO-MM]](https://github.com/OpenMOSS/GAOKAO-MM)：基于中国高考题的多模态数据集，测评多模态模型的感知、理解、知识、推理能力。
[[GAOKAO-Bench-Updates]](https://github.com/OpenLMLab/GAOKAO-Bench-Updates):将中国2023年及之后的高考选择题作为数据集 ，对GAOKAO-Bench的补充。
## 介绍
我们希望能够建立一个标准化、综合性的评测框架来对大模型进行全方位、准确的评估。在中国，高考是标准化水平最高、综合性最强并且认可度最广的考试之一，我们希望借用高考的题目来评估大模型的能力。因此，我们收集了2010-2022年全国高考卷的题目，其中包括1781道客观题和1030道主观题，构建起GAOKAO-Bench的数据部分。
## 数据集
| 题目类型 | 题目数量 | 数量占比 |
| ----------------- | ------------- | ------------- |
| 客观题 | 1781 | 63.36% |
| 主观题 | 1030 | 36.64% |
| **题目总数** | **2811** | **100%** |
数据示例如下所示：
- **Year**
> 2022
- **Category**
> 全国甲卷
>
- **Score**
> 5
- **Question**
> 若 $z=-1+\sqrt{3} \mathrm{i}$, 则 $\frac{z}{z \bar{z}-1}=()$
>
> A. $-1+\sqrt{3} \mathrm{i}$
>
> B. $-1-\sqrt{3} i$
>
> C. $-\frac{1}{3}+\frac{\sqrt{3}}{3} \mathrm{i}$
>
> D. $-\frac{1}{3}-\frac{\sqrt{3}}{3} i$
>
- **Analysis**
> 【详解】
>
> $\bar{z}=-1-\sqrt{3} i, z \bar{z}=(-1+\sqrt{3} i)(-1-\sqrt{3} i)=1+3=4$.
>
> $\frac{z}{z \bar{z}-1}=\frac{-1+\sqrt{3} \mathrm{i}}{3}=-\frac{1}{3}+\frac{\sqrt{3}}{3} \mathrm{i}$
>
> 故选: C
>
* **Standard Answer**
> C
## 测试结果
### 高考总分
我们采用zero-shot的方式测试各项模型，对客观题采用基于规则的答案抽取方式，对主观题采取人工评阅的方式，最终获得了GPT-4、GPT-3.5等模型的转化后的高考总分。实验结果表明，GPT-4转换后的高考总分名列第一，文科和理科总分分别为485和447。同时，所有模型的文科成绩都高于理科成绩。
### 客观题得分率
| **Models** | **Overall** | **Chinese** | **Eng.** | **Sci. Math** | **Hum. Math** | **Phys.** | **Chem.** | **Biol.** | **Poli.** | **Hist.** | **Geog.** |
| --------------------------------------------- | ----------- | ----------- | --------- | ------------- | ------------- | --------- | --------- | --------- | --------- | --------- | --------- |
| **GPT-4-0314** | **72.2%** | **53.9%** | 93.1% | 53.7% | 63.3% | **55.5%** | 44.4% | 80.7% | 75.9% | 75.6% | 80.0% |
| **GPT-4-0613** | 71.6% | 52.1% | **93.2%** | **54.5%** | **64.0%** | 50.8% | 43.6% | **83.0%** | 72.5% | 74.2% | **81.1%** |
| **Gemini-Pro** | 57.9% | 46.7% | 69.9% | 40.7% | 47.7% | 32.0% | 40.3% | 70.7% | 64.7% | 64.5% | 68.4% |
| **ERNIE-Bot-0615** | 56.6% | 46.7% | 31.0% | 38.3% | 49.1% | 35.9% | **66.1%** | 79.3% | **86.9%** | **79.1%** | 68.4% |
| **GPT-3.5-turbo-0301** | 53.2% | 34.7% | 76.6% | 38.8% | 47.8% | 41.1% | 38.7% | 56.9% | 45.3% | 53.9% | 54.0% |
| **ERNIE-Bot-turbo-0725** | 45.6% | 35.3% | 26.6% | 34.1% | 36.2% | 32.0% | 51.6% | 64.0% | 72.2% | 63.4% | 44.2% |
| **Baichuan2-13b-Chat** | 43.9% | 26.9% | 34.7% | 23.8% | 31.7% | 25.0% | 40.3% | 53.3% | 75.3% | 59.9% | 61.1% |
| **ChatGLM2-6b** | 42.7% | 31.1% | 30.6% | 29.0% | 35.8% | 24.2% | 46.0% | 71.3% | 55.0% | 59.2% | 41.1% |
| **Baichuan2-7b-Chat** | 40.5% | 31.7% | 33.0% | 26.6% | 28.4% | 18.0% | 26.6% | 48.0% | 69.7% | 57.8% | 49.5% |
| **ChatGLM-6b** | 30.8% | 18.6% | 17.0% | 25.2% | 25.7% | 12.5% | 30.6% | 24.7% | 54.1% | 59.9% | 25.3% |
| **Baichuan2-7b-Base** | 27.2% | 16.2% | 21.2% | 24.8% | 24.8% | 0.0% | 23.4% | 24.0% | 55.3% | 32.1% | 24.2% |
| **LLaMA-7b** | 21.1% | 16.2% | 20.5% | 24.3% | 26.1% | 0.0% | 22.6% | 22.7% | 22.2% | 19.2% | 24.2% |
| **Vicuna-7b** | 21.0% | 12.0% | 19.6% | 23.8% | 23.4% | 7.0% | 27.4% | 20.0% | 20.9% | 23.0% | 23.2% |
### 主观题得分率
| **Models** | **Overall** | **Chinese** | **Eng.** | **Sci. Math** | **Hum. Math** | **Phys.** | **Chem.** | **Biol.** | **Poli.** | **Hist.** | **Geog.** |
| --------------------------------------------- | ----------- | ----------- | --------- | ------------- | ------------- | --------- | --------- | --------- | --------- | --------- | --------- |
| **GPT-4-0314** | **51.9%** | 51.5% | **88.3%** | 24.1% | **27.9%** | **56.7%** | **35.0%** | **85.6%** | 50.0% | **63.1%** | 70.0% |
| **GPT-4-0613** | 50.8% | 50.3% | 87.6% | **24.6%** | 27.5% | 47.1% | 28.5% | **85.6%** | 49.9% | 59.9% | 71.5% |
| **ERNIE-Bot-0615** | 48.4% | **57.1%** | 45.0% | 17.0% | 25.6% | 33.5% | 30.8% | 84.9% | **53.0%** | 60.0% | **72.7%** |
| **ERNIE-Bot-turbo-0725** | 39.2% | 42.5% | 28.8% | 14.6% | 15.6% | 23.2% | 25.0% | 85.1% | 45.3% | 47.0% | 61.8% |
| **GPT-3.5-turbo-0301** | 35.8% | 33.9% | 75.4% | 15.2% | 15.9% | 16.9% | 21.4% | 36.3% | 42.3% | 58.4% | 62.1% |
## 简单示例
#### Openai API
1. 获取GPT-4模型输出
```
cd ./Bench
## Get the Output of Objective Questions
python objective_bench.py --openai_api_key="your openai api key"
## Get the Output of Subjective Questions
python subjective_bench.py --openai_api_key="your openai api key"
```
2. 计算GPT-4模型客观题得分率
* 将GPT-4模型输出的JSON文件存放在`./Results/gpt_4_obj`文件夹下。
* 执行以下指令，获得其客观题的得分率，结果存放在`./Results/gpt_4_obj/result/correction_score.json`文件下。
```
python OBJ_score_evaluation.py --obj_output_dir=../Results/gpt_4_obj
```
3. 计算GPT-4模型主观题得分率
由于人工批改的高昂成本，我们提供了LLM-as-a-Judge脚本，利用GPT-4-turbo为模型的主观题打分。
* 将GPT-4模型输出的JSON文件存放在`./Results/gpt_4_sub`文件夹下。
* 执行以下指令，获得GPT-4对主观题的评分，结果存放在`./Results/gpt_4_sub/gpt-4-1106-preview_correction_wo_marking_criterion`文件下。
```
python subjective_grade.py --openai_api_key="your openai api key"
```
* 执行以下指令，获得其主观题的得分率，结果存放在`./Results/gpt_4_sub/gpt-4-1106-preview_correction_wo_marking_criterion/result/model_score.json`文件下。
```
python SUB_score_evaluation.py --sub_output_dir=../Results/gpt_4_sub/gpt-4-1106-preview_correction_wo_marking_criterion --mode=model
```
4. 计算GPT-4模型高考总分
执行以下指令，获得GPT-4转换后的高考总分，结果保存在`./Results/merge_score.json`下。
```
python merge_OBJ_SUB_score.py
```
#### 其他模型
封装你的模型API并放置在 `./Models` 目录下，封装方式可参考`./Models/openai_gpt4.py`。
## 引用
```
@inproceedings{Zhang2023EvaluatingTP,
title={Evaluating the Performance of Large Language Models on GAOKAO Benchmark},
author={Xiaotian Zhang and Chunyang Li and Yi Zong and Zhengyu Ying and Liang He and Xipeng Qiu},
year={2023}
}
```
## 致谢
我们非常感谢上海市曹杨第二中学的老师们，他们负责了GAOKAO-Bench主观题部分的评分。